Journal of Applied Science, Engineering, Technology and Management

AI-Based Smart Stick for the Visually Impaired

2026-01-04T16:35:23+00:00

Visually impaired individuals face numerous challenges while navigating their environment. Traditional mobility aids, such as white canes, provide limited assistance in detecting obstacles. To enhance their independence and safety, smart assistive technologies are needed. This paper presents a Smart Blind Stick designed to assist visually impaired individuals by identifying objects in their surroundings and providing real-time audio feedback. The system integrates an ESP32 camera module and a Raspberry Pi, utilizing YOLO (You Only Look Once) object detection for image processing. The ESP32 is mounted on the blind stick to capture real-time images, which are transmitted to the Raspberry Pi for analysis. Upon detecting and identifying objects, the system generates an audio message and conveys it to the user via headphones, enhancing their awareness of obstacles and surroundings. This innovation aims to improve mobility, safety, and independence for the visually impaired through affordable and efficient technology.

IoT Based Real-Time Measuring and Monitoring System for Irrigation Canals

2026-01-04T08:11:19+00:00

Efficient water resource management is vital for sustainable agriculture, particularly in the operation of irrigation canals. This project introduces an Internet of Things (IoT)-based system designed to provide real-time data on water discharge within these canals, aiming to enhance distribution efficiency and minimize wastage. The system features a user-friendly mobile application, Flow Tracker, which offers real-time visualization of water levels and discharge rates, empowering users to make informed decisions promptly. To accommodate diverse operational environments, the system supports multiple power input options, including DC power input, rechargeable lithium batteries, and ESP module charging. Field tests have demonstrated the system’s accuracy and reliability across various environmental conditions. By integrating IoT technology with real-time data analytics and intuitive interfaces, this system represents a significant advancement over traditional discharge measurement methods, addressing common challenges in irrigation management and promoting the sustainable use of water resources.

AI driven Foundation and Material Recommendation for Sustainable Construction

2026-01-04T08:52:30+00:00

In the realm of sustainable architecture and civil engineering, optimizing foundational and material components is pivotal for achieving structural stability and environmental efficiency. An innovative AI-driven parametric design tool tailored to address these crucial aspects by leveraging real-time environmental data and material properties. The tool aims to enhance decision-making, reduce carbon footprint, and ensure cost-effective construction practices. The Foundation Model plays a vital role by utilizing comprehensive soil data, including specific gravity, and moisture content, collected from multiple borehole samples. Processed through SQLite, this model provides precise foundation type recommendations, ensuring stability and cost efficiency under diverse geological conditions. The Material Model evaluates essential material properties such as thermal conductivity, water absorption, permeability, and durability. It emphasizes selecting sustainable building materials tailored to different climate zones, promoting energy efficiency and reducing environmental impact. By focusing on these two core components, the project demonstrates the potential of AI in transforming traditional construction methodologies. This offers a forward-thinking approach to material and foundation optimization, contributing to the advancement of sustainable architecture. Through data-driven insights and automation, it paves the way for more resilient and eco-friendly building designs

Integration of Sensor and GPS System in Multisource Electric Vehicle for Accident Control and Safety

2026-01-04T17:04:47+00:00

Nowadays, the major reason for road accidents is over speed, fire accidents, and driver’s negligence. This research study intends to overcome these challenges by using fire detection and accident detection. If in case any accidents occur, it sends instant SMS and live location to the related Road centers like RTC centers, hospitals, fire stations, etc., This makes the accident the location to be immediately known and it helps to save valuable lives. The flame sensor is used for fire detection. The paper is based on the ARDUINO, all the sensors are connected to it and send SMS of the live location through the GPS and GSM module. The vehicle accident location tracking system integrates several safety features using GSM (Global System for Mobile Communications) and GPS (Global Positioning System) technologies. The primary objective of the system is to enhance passenger and vehicle safety by addressing the major causes of road accidents such as over speeding and fire accidents. The system is designed to detect accidents and immediately notify relevant authorities by sending SMS alerts with the live location of the incident to road centers, hospitals, and fire stations. This rapid communication is intended to ensure that the accident site is quickly identified, thereby facilitating prompt rescue operations and potentially saving lives. Overall, the system aims to reduce the impact of road accidents by ensuring timely detection and communication, thereby improving the safety of passengers and vehicles. This system is particularly useful in scenarios where accidents occur in remote areas, where timely detection and response are crucial. By leveraging advanced technologies like GPS, GSM, and various sensors, the proposed system represents a significant step forward in improving road safety.

Real-Time Wireless Tyre Pressure Monitoring System (TPMS) for Enhanced Vehicle Safety

2026-01-11T18:13:29+00:00

The Automatic tyre Pressure Monitoring System (TPMS) is an advanced safety and efficiency-enhancing system designed to continuously monitor the air pressure in vehicle tyres. It utilizes pressure sensors installed within the tyres to detect pressure variations and transmits real-time data wirelessly to a dashboard display or a mobile application. Upon detecting deviations from the optimal pressure range, the system promptly alerts the driver, enabling timely corrective action. By ensuring proper tyre inflation, TPMS enhances road safety, fuel efficiency, and tyre longevity, reducing the risks associated with underinflated or over-inflated tyres, such as blowouts and increased wear. The system operates using direct or indirect monitoring methods—direct TPMS employs dedicated pressure sensors, while indirect TPMS utilizes wheel speed data from the Anti-lock Braking System (ABS) to estimate pressure changes. The implementation of TPMS contributes to enhanced vehicle performance, reduced maintenance costs, and lower environmental impact by optimizing fuel consumption and minimizing carbon emissions. This system is an essential feature in modern automobiles, promoting safer and more efficient driving conditions.

Design and Implementation of an AI and IoT Enabled Solar-Powered Robotic Lawn Mower

2026-01-27T08:00:32+00:00

The increasing demand for sustainable and autonomous lawn maintenance systems has accelerated the development of intelligent robotic solutions. This paper presents the design and implementation of an AI and IoT based solar-powered robotic lawn mower capable of autonomous navigation, grass cutting, and waste collection. The proposed system utilizes renewable solar energy combined with a rechargeable battery to ensure eco-friendly and energy-efficient operation. An ESP32 microcontroller manages motor control, navigation, and obstacle avoidance, while a Raspberry Pi 4 handles intelligent decision-making and robotic arm operation for waste collection.

Ultrasonic sensors are employed for real-time obstacle detection, ensuring safe and adaptive movement across outdoor environments. A robotic arm equipped with a high-torque servomotor is integrated to detect and collect plastic waste, enhancing environmental cleanliness. IoT connectivity enables real-time monitoring of system status, battery level, and operational control through a web-based interface.

Experimental evaluation demonstrates improved lawn coverage, reduced redundant motion, and efficient power utilization. The integration of solar energy significantly reduces dependence on conventional power sources, making the system cost-effective and environmentally sustainable. The results confirm that AI-driven navigation and IoT-based monitoring enhance operational reliability and user convenience. This work highlights the potential of intelligent robotic systems in sustainable lawn maintenance applications.

Design and Development of Scootico: A Compact Single-Seater Electric Scooter for Urban Mobility

2026-01-27T08:08:31+00:00

This paper delves into the conception, design, and development of an eco-friendly electric scooter tailored to meet the growing demands for sustainable urban transportation. By emphasizing portability, efficiency, and sustainability, this scooter incorporates advanced features such as a hub motor for high efficiency, sealed lead-acid batteries for affordability and dynamic riding comfort. The engineering challenges faced during development, including power consumption optimization, thermal management, and cost efficiency, were meticulously addressed through innovative solutions to ensure reliability and practicality.

Further, the project envisions future enhancements such as solar auxiliary charging to improve energy autonomy and a foldable design to increase portability. Inspired by the mobility constraints observed during the COVID-19 pandemic, this scooter presents an ideal solution for short-distance travel, especially in regions where public transport options are scarce. It not only provides a practical means of transportation but also contributes significantly to reducing carbon emissions and promoting sustainable living.